SoroushVahidi/scidocs huggingface dataset:
M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments
(2018)

In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems. The M2U-Net has a new encoder-decoder architecture that is inspired by the U-Net. It adds pretrained components of MobileNetV2 in the encoder part and novel contractive bottleneck blocks in the decoder part that, combined with bilinear upsampling, drastically reduce the parameter count to 0.55M compared to 31.03M in the original U-Net. We have evaluated its performance against a wide body of previously published results on three public datasets. On two of them, the M2U-Net achieves new state-of-the-art performance by a considerable margin. When implemented on a GPU, our method is the first to achieve real-time inference speeds on high-resolution fundus images. We also implemented our proposed network on an ARM-based embedded system where it segments images in between 0.6 and 15 sec, depending on the resolution. Thus, the M2U-Net enables a number of applications of retinal vessel structure extraction, such as early diagnosis of eye diseases, retinal biometric authentication systems, and robot assisted microsurgery.
GIS METHODS IN TIME-GEOGRAPHIC RESEARCH: GEOCOMPUTATION AND GEOVISUALIZATION OF HUMAN ACTIVITY PATTERNS
(2004)

Over the past 40 years or so, human activities and movements in space-time have attracted considerable research interest in geography. One of the earliest analytical perspectives for the analysis of human activity patterns and movements in spacetime is time geography. Despite the usefulness of time geography in many areas of geographical research, there are very few studies that actually implemented its constructs as analytical methods up to the mid-1990s. With increasing availability of geo-referenced individual-level data and improvement in the geo-computational capabilities of Geographical Information Systems (GIS), it is now more feasible than ever before to operationalize and implement time-geographic constructs. This paper discusses recent applications of GIS-based geo-computation and three-dimensional (3-D) geo-visualization methods in time-geographic research. The usefulness of these methods is illustrated through examples drawn from the author’s recent studies. The paper attempts to show that GIS provides an effective environment for implementing timegeographic constructs and for the future development of operational methods in time-geographic research.
SHiP: Signature-based Hit Predictor for high performance caching
(2011)

The shared last-level caches in CMPs play an important role in improving application performance and reducing off-chip memory bandwidth requirements. In order to use LLCs more efficiently, recent research has shown that changing the re-reference prediction on cache insertions and cache hits can significantly improve cache performance. A fundamental challenge, however, is how to best predict the re-reference pattern of an incoming cache line.
 This paper shows that cache performance can be improved by correlating the re-reference behavior of a cache line with a unique signature. We investigate the use of memory region, program counter, and instruction sequence history based signatures. We also propose a novel Signature-based Hit Predictor (SHiP) to learn the re-reference behavior of cache lines belonging to each signature. Overall, we find that SHiP offers substantial improvements over the baseline LRU replacement and state-of-the-art replacement policy proposals. On average, SHiP improves sequential and multiprogrammed application performance by roughly 10% and 12% over LRU replacement, respectively. Compared to recent replacement policy proposals such as Seg-LRU and SDBP, SHiP nearly doubles the performance gains while requiring less hardware overhead.
A Multiband Dual-Polarized Omnidirectional Antenna for 2G/3G/LTE Applications
(2018)

A multiband dual-polarized omnidirectional antenna for 2G/3G/Long Term Evolution (LTE) mobile communications is proposed in this letter, which consists of horizontal polarization (HP) and vertical polarization (VP) element with separate feeds. The VP element consists of three polygonal radiation patches with three equally spaced legs shorted to the ground plane. The HP element consists of three wideband slot loop structures, which is nested on the top of the VP element. Three slot loop structures provide a 360° coverage for HP and enhance its bandwidth. Both the simulated and measured results indicate that the frequency bands of 1650–2900 MHz for HP and 780–2700 MHz for VP can be achieved. The reflection of VP improves the gain of the HP element at least 1 dBi after nesting. The gain of HP element is more than 3 dBi for LTE, and the gain of VP element is more than 5 dBi in the LTE band and 1.5 dBi in the 2G band. Port isolation larger than 30 dB and low-gain variation levels are also obtained. The proposed antenna can be applied in mobile communications.
Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm
(2014)

Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.
Social Honeypots: Making Friends With A Spammer Near You
(2008)

Social networking communities have become an important communications platform, but the popularity of these communities has also made them targets for a new breed of social spammers. Unfortunately, little is known about these social spammers, their level of sophistication, or their strategies and tactics. Thus, in this paper, we provide the first characterization of social spammers and their behaviors. Concretely, we make two contributions: (1) we introduce social honeypots for tracking and monitoring social spam, and (2) we report the results of an analysis performed on spam data that was harvested by our social honeypots. Based on our analysis, we find that the behaviors of social spammers exhibit recognizable temporal and geographic patterns and that social spam content contains various distinguishing characteristics. These results are quite promising and suggest that our analysis techniques may be used to automatically identify social spam.
Monolithic DC-DC boost converter with current-mode hysteretic control
(2011)

A monolithic DC-DC boost converter with current-mode hysteretic control is designed and simulated in 0.18-μm CMOS technology. The system is simple, robust, and has a fast response to external changes. It does not require an external clock, and the output is regulated by voltage feedback in addition to limiting the inductor current by sensing it. A non-overlapping clock is internally generated to drive the power switches using buffers designed to minimize power dissipation. For conversion specifications of 1.8 V to 3.3 V at 150 mA, overall efficiency of 94.5% is achieved. Line regulation is 17.5 mV/V, load regulation is 0.33% for a 100 mA current step, while the output voltage ripple is below 30 mV for nominal conditions.
Least-Squares Fitting of Circles and Ellipses ∗
(1994)

Fitting circles and ellipses to given points in the plane is a problem that arises in many application areas, e.g. computer graphics [1], coordinate metrology [2], petroleum engineering [11], statistics [7]. In the past, algorithms have been given which fit circles and ellipses in some least squares sense without minimizing the geometric distance to the given points [1], [6]. In this paper we present several algorithms which compute the ellipse for which the sum of the squares of the distances to the given points is minimal. These algorithms are compared with classical simple and iterative methods. Circles and ellipses may be represented algebraically i.e. by an equation of the form F (x) = 0. If a point is on the curve then its coordinates x are a zero of the function F . Alternatively, curves may be represented in parametric form, which is well suited for minimizing the sum of the squares of the distances. 1 Preliminaries and Introduction Ellipses, for which the sum of the squares of the distances to the given points is minimal will be referred to as “best fit” or “geometric fit”, and the algorithms will be called “geometric”. Determining the parameters of the algebraic equation F (x) = 0 in the least squares sense will be denoted by “algebraic fit” and the algorithms will be called “algebraic”. We will use the well known Gauss-Newton method to solve the nonlinear least squares problem (cf. [15]). Let u = (u1, . . . , un) T be a vector of unknowns and consider the nonlinear system of m equations f(u) = 0. If m > n, then we want to minimize
Measures of retaining digital evidence to prosecute computer-based cyber-crimes
(2007)

With the rapid growth of computer and network systems in recent years, there has also been a corresponding increase in cyber-crime. Cybercrime takes many forms and has garnered much attention in the media, making information security a more urgent and important priority. In order to fight cyber-crime, criminal evidence must be gathered from these computer-based systems. This is quite different from the collection of conventional criminal evidence and can confuse investigators attempting to deal with the forensics of cyber-crime, highlighting the importance of computer forensics. In this paper, we offer solutions to guard against cyber-crime through the implementation of software toolkits for computerbased systems. In this way, those who engage in criminal acts in cyber-space can be more easily apprehended. © 2006 Elsevier B.V. All rights reserved.
Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men.
(1994)

OBJECTIVE
To investigate the relation between obesity, fat distribution, and weight gain through adulthood and the risk of non-insulin-dependent diabetes mellitus (NIDDM).


RESEARCH DESIGN AND METHODS
We analyzed data from a cohort of 51,529 U.S. male health professionals, 40-75 years of age in 1986, who completed biennial questionnaires sent out in 1986, 1988, 1990, and 1992. During 5 years of follow-up (1987-1992), 272 cases of NIDDM were diagnosed among men without a history of diabetes, heart disease, and cancer in 1986 and who provided complete health information. Relative risks (RRs) associated with different anthropometric measures were calculated controlling for age, and multivariate RRs were calculated controlling for smoking, family history of diabetes, and age.


RESULTS
We found a strong positive association between overall obesity as measured by body mass index (BMI) and risk of diabetes. Men with a BMI of > or = 35 kg/m2 had a multivariate RR of 42.1 (95% confidence interval [CI] 22.0-80.6) compared with men with a BMI < 23.0 kg/m2. BMI at age 21 and absolute weight gain throughout adulthood were also significant independent risk factors for diabetes. Fat distribution, measured by waist-to-hip ratio (WHR), was a good predictor of diabetes only among the top 5%, while waist circumference was positively associated with the risk of diabetes among the top 20% of the cohort.


CONCLUSIONS
These data suggest that waist circumference may be a better indicator than WHR of the relationship between abdominal adiposity and risk of diabetes. Although early obesity, absolute weight gain throughout adulthood, and waist circumference were good predictors of diabetes, attained BMI was the dominant risk factor for NIDDM; even men of average relative weight had significantly elevated RRs.
A Highly Linear Two-Stage Amplifier Integrated Circuit Using InGaP/GaAs HBT
(2010)

This paper presents a linearized two-stage amplifier integrated circuit (IC), having an output power level of about 1 Watt, used for general purpose applications. A predistortion method, based on alignment of the nonlinear characteristics between the first- and the second-stage amplifiers, has been proposed and analyzed in order to enhance the linearization aspects. The resistors in the active bias circuits of both the stages are optimized to achieve the best cancellation conditions for the third-order intermodulation components. The two-stage amplifier IC, which is based on an InGaP/GaAs hetero-junction bipolar transistor (HBT) technology, has been designed and implemented for the 900 MHz band. An output 1 dB compression point (P1dB) of 29.6 dBm, a maximum third-order output intercept point (OIP3) of as high as 48.7 dBm at a two-tone average output power of 21 dBm have been obtained while having a quiescent current of 375 mA and a single bias supply of 5.5 V. The implemented amplifier is able to maintain its IMD3 performance below -60 dBc up to an output power of 21 dBm.
REAL TIME HAND GESTURE RECOGNITION USING SIFT
(2012)

The objective of the gesture recognition is to identify and distinguish the human gestures and utilizes these identified gestures for applications in specific domain. In this paper we propose a new approach to build a real time system to identify the standard gesture given by American Sign Language, or ASL, the dominant sign language of Deaf Americans, including deaf communities in the United States, in the English-speaking parts of Canada, and in some regions of Mexico. We propose a new method of improvised scale invariant feature transform (SIFT) and use the same to extract the features. The objective of the paper is to decode a gesture video into the appropriate alphabets.
Improved Training of Generative Adversarial Networks Using Representative Features
(2018)

Despite the success of generative adversarial networks (GANs) for image generation, the trade-off between visual quality and image diversity remains a significant issue. This paper achieves both aims simultaneously by improving the stability of training GANs. The key idea of the proposed approach is to implicitly regularize the discriminator using representative features. Focusing on the fact that standard GAN minimizes reverse Kullback-Leibler (KL) divergence, we transfer the representative feature, which is extracted from the data distribution using a pre-trained autoencoder (AE), to the discriminator of standard GANs. Because the AE learns to minimize forward KL divergence, our GAN training with representative features is influenced by both reverse and forward KL divergence. Consequently, the proposed approach is verified to improve visual quality and diversity of state of the art GANs using extensive evaluations.
Functional inactivation of the amygdala before but not after auditory fear conditioning prevents memory formation.
(1999)

Two competing theories predict different effects on memory consolidation when the amygdala is inactivated after fear conditioning. One theory, based on studies using inhibitory avoidance training, proposes that the amygdala modulates the strength of fear learning, and post-training amygdala manipulations interfere with memory consolidation. The other, based on studies using Pavlovian fear conditioning, hypothesizes that fear learning occurs in the amygdala, and post-training manipulations after acquisition will not affect memory consolidation. We infused the GABAA agonist muscimol (4.4 nmol/side) or vehicle into lateral and basal amygdala (LBA) of rats either before or immediately after tone-foot shock Pavlovian fear conditioning. Pre-training infusions eliminated acquisition, whereas post-training infusions had no effect. These findings indicate that synaptic activity in LBA is necessary during learning, but that amygdala inactivation directly after training does not affect memory consolidation. Results suggest that essential aspects of plasticity underlying auditory fear conditioning take place within LBA during learning.
BitDeposit: Deterring Attacks and Abuses of Cloud Computing Services through Economic Measures
(2013)

Dependability in cloud computing applications can be negatively affected by various attacks or service abuses. To come ahead of this threat, we propose an economic measure to deter attacks and various service abuses in cloud computing applications. Our proposed defense is based on requiring a service user to pay a small deposit, using digital currency, before invoking the service. Once they are done using the service, and there has been no detected abuse or attack, the deposit is paid back by the service provider to the service user. If an attack or an abuse is detected, the service user is not paid back and the service provider gets to keep the deposit. We propose the use of micro payments with a decentralized nature and small transaction fees, such as the Bit coin digital currency. Moreover, thanks to the existence of money exchanges which convert the Bit coin currency to real world currency, service providers can recoup loses when they exchange the confiscated deposits for real world currency.
Multi-channel ECG classification using forests of randomized shapelet trees
(2015)

Data series of multiple channels occur at high rates and in massive quantities in several application domains, such as healthcare. In this paper, we study the problem of multi-channel ECG classification. We map this problem to multivariate data series classification and propose five methods for solving it, using a split-and-combine approach. The proposed framework is evaluated using three base-classifiers on real-world data for detecting Myocardial Infarction. Extensive experiments are performed on real ECG data extracted from the Physiobank data repository. Our findings emphasize the importance of selecting an appropriate base-classifier for multivariate data series classification, while demonstrating the superiority of the Random Shapelet Forest (0.825 accuracy) against competitor methods (0.664 accuracy for 1-NN under cDTW).
Getting Physical with the Digital Investigation Process
(2003)

In this paper, a process model for digital investigations is defined using the theories and techniques from the physical investigation world. While digital investigations have recently become more common, physical investigations have existed for thousands of years and the experience from them can be applied to the digital world. This paper introduces the notion of a digital crime scene with its own witnesses, evidence, and events that can be investigated using the same model as a physical crime scene. The proposed model integrates the physical crime scene investigation with the digital crime scene investigation to identify a person who is responsible for the digital activity. The proposed model applies to both law enforcement and corporate investigations.
Digital Forensic Model Based On Malaysian Investigation Process
(2009)

Faculty Of Science & Technology Islamic Science University Of Malaysia Summary With the proliferation of the digital crime around the world, numerous digital forensic investigation models already being develop .In fact many of the digital forensic investigation model focus on technical implementation of the investigation process as most of it develop by traditional forensic expert and technologist. As an outcome of this problem most of the digital forensic practitioners focus on technical aspect and forget the core concept of digital forensic investigation model .In this paper we are introducing a new digital forensic model which will be capture a full scope of an investigation process based on Malaysia Cyber Law .The proposed model is also compared with the existing model which currently available and being apply in the investigation process.
A hierarchical, objectives-based framework for the digital investigations process
(2005)

Although digital forensics investigations can vary drastically in their level of complexity, each investigative process must follow a rigorous path. Previously proposed frameworks are predominantly single-tier, higher order process models, focusing on the abstract, rather than more concrete principles of the investigation. We contend that these frameworks, although useful in explaining overarching concepts, require additional detail in order to be useful to investigators and researchers. We therefore propose a multi-tier, hierarchical framework to guide digital investigations. Our framework includes objectivesbased sub-phases that are applicable to various layers of abstraction.
An Extended Model of Cybercrime Investigations
(2004)

A comprehensive model of cybercrime investigations is important for standardising terminology, defining requirements, and supporting the development of new techniques and tools for investigators. In this paper a model of investigations is presented which combines the existing models, generalises them, and extends them by explicitly addressing certain activities not included in them. Unlike previous models, this model explicitly represents the information flows in an investigation and captures the full scope of an investigation, rather than only the processing of evidence. The results of an evaluation of the model by practicing cybercrime investigators are presented. This new model is compared to some important existing models and applied to a real investigation.
Defining Digital Forensic Examination and Analysis Tool Using Abstraction Layers
(2003)

This paper uses the theory of abstraction layers to describe the purpose and goals of digital forensic analysis tools. Using abstraction layers, we identify where tools can introduce errors and provide requirements that the tools must follow. Categories of forensic analysis types are also defined based on the abstraction layers. Abstraction layers are not a new concept, but their usage in digital forensic analysis is not well documented.
Faster Dual-Key Stealth Address for Blockchain-Based Internet of Things Systems
(2018)

Stealth address prevents public association of a blockchain transaction’s output with a recipient’s wallet address and hides the actual destination address of a transaction. While stealth address provides an effective privacy-enhancing technology for a cryptocurrency network, it requires blockchain nodes to actively monitor all the transactions and compute the purported destination addresses, which restricts its application for resource-constrained environments like Internet of Things (IoT). In this paper, we propose DKSAP-IoT, a faster dual-key stealth address protocol for blockchain-based IoT systems. DKSAP-IoT utilizes a technique similar to the TLS session resumption to improve the performance and reduce the transaction size at the same time between two communication peers. Our theoretical analysis as well as the extensive experiments on an embedded computing platform demonstrate that DKSAPIoT is able to reduce the computational overhead by at least 50% when compared to the state-of-the-art scheme, thereby paving the way for its application to blockchain-based IoT systems.